Search Results (1,583)

This paper presents a methodology for synthesizing static state feedback controllers utilizing a Fractional-Order Performance Index. Linear Quadratic Regulators are designed using integer-order integral weighting functions. In the proposed approach, fractional-order calculus is utilized to introduce an additional degree of freedom in controller synthesis, enabling enhanced shaping of the plant’s dynamic properties. The controller gains are obtained by solving a fractional Riccati-like equation, through which the temporal weighting properties inherent to fractional integration are embedded into a static feedback matrix. This formulation is a minimalist control structure suitable for implementation on resource-constrained hardware. The proposed method is validated via rapid control prototyping on an industrial NI PXIe platform and an analog third-order plant. Performance evaluation using Integral Absolute Error and Integral Absolute Control metrics demonstrates that the fractional order serves as a flexible tuning parameter, providing an alternative trade-off between settling time and control effort. Furthermore, frequency domain sensitivity analysis demonstrates the absence of resonant peaks and inherent attenuation of high-frequency measurement noise. As a result, the presented framework bridges fractional-order optimization techniques with industrial control platforms. Full article

The deployment of wall-climbing robotic systems plays an important role for executing inspection and maintenance tasks in high-risk environments and minimizing the risk to operators tasked with the inspection. Conventional adhesion techniques, such as magnetic, suction, and dry adhesives, encounter significant challenges when applied to diverse surface types. This study presents a four-wheeled robotic platform utilizing dual electric ducted fans (EDFs) to produce adjustable adhesion forces, facilitating uninterrupted movement from horizontal to vertical planes. A comprehensive multibody dynamics model constructed using MSC Adams analyzed wheel–surface interaction, thrust forces, and system stability during transitional phases, revealing essential force parameters for stable vertical operation and determining minimum thrust levels required to sustain four-point contact during orthogonal transitions. These findings informed thrust distribution optimization between the two EDF units to reduce rotational effects while ensuring sufficient safety margins during the ground to vertical wall transition. The findings also allowed for appropriate thrust application ensuring the generation of the required normal force distribution at wheel contact interfaces during vertical movement. A physical prototype was developed and experimentally validated, demonstrating dependable adhesion and maneuverability across a spectrum of orientations and highlighting the efficacy of simulation-driven design for thrust-based adhesion systems. Full article

Autonomous path planning for unmanned aerial vehicles (UAVs) in complex three-dimensional environments requires a balance among search efficiency, obstacle avoidance safety, and trajectory smoothness. However, conventional A* algorithms often suffer from redundant node expansion, insufficient safety awareness, and poor turning performance. To overcome these limitations, this study proposes a hierarchical hybrid planning framework that integrates an improved A* algorithm, particle swarm optimization (PSO), and B-spline trajectory generation. In the global planning stage, a composite cost function is designed by considering path length, safety margin, and turning penalty. Meanwhile, a directional dynamic window and Top- K candidate selection strategy are introduced to reduce invalid expansions and improve search efficiency. In the local refinement stage, key turning regions along the coarse path are identified and optimized using an improved PSO method with adaptive inertia attenuation, reflective boundary handling, and stagnation-triggered reseeding. Finally, B-spline fitting is applied to generate a continuous and executable UAV trajectory. Simulation results show that all compared methods achieved a 100% success rate in the randomized environments. The proposed framework achieved a mean runtime of 20.664 s, compared with 47.108 s for standard A* and 134.666 s for composite-cost A*. Meanwhile, it maintained a comparable path length, indicating robust feasible-path generation, preserved path quality, and acceptable computational feasibility under the tested randomized environments. Full article

During the start stage of ship welding, obtaining the three-dimensional coordinates of welding target points is affected by confined installation space, surface reflection, and deployment constraints. This paper proposes a low-complexity point-wise three-dimensional localization method based on two-dimensional visual planar guidance and one-dimensional point-laser distance constraints. A direct computation model of the laser incident point in the robot base coordinate system is established from the tool center point pose, the extrinsic parameters of the point-laser module, and real-time ranging data, enabling target-point coordinate estimation without dense three-dimensional reconstruction. A dual-stage stabilization strategy is introduced by combining ranging-level filtering, spatial coordinate smoothing, and outlier suppression. Image error-based visual closed-loop alignment is further used as a pre-measurement step to ensure that the point laser acts on the target region. Experimental results show that, after workplane-level extrinsic correction, independent validation points achieve a mean three-dimensional Euclidean error of 1.54 mm with a standard deviation of 0.28 mm. The average planar error in closed-loop alignment experiments is 1.124 mm. Passive binocular depth measurement on the current platform still yields an RMSE of 6.16 mm after linear correction. A simulated fillet-weld task verifies the feasibility of the complete perception-to-execution workflow. The proposed method provides a low-complexity coordinate acquisition route for discrete welding target points before arc ignition. Full article

Single-view RGB-D observations are often affected by occlusion and restricted viewpoints, leading to incomplete object geometry and underestimated obstacle extents in indoor robot perception. This paper proposes a single-view scene completion framework that integrates candidate model retrieval and scale-aware registration. The framework first generates local RGB crops and partial point clouds through automatic instance segmentation; then retrieves complete candidate models by matching the local crops with multi-view rendered CAD images; and finally estimates candidate-to-observation rotation, translation, and scale to insert the selected aligned model into the original scene coordinate system. Experiments show that the retrieval module achieves Recall@1/Recall@5 of 80%/89%. The registration module reaches a success rate of 56.61%, outperforming the second-best method by 12.28 percentage points. More importantly, scene-level evaluation shows that the proposed method improves occupancy F1 from 0.445 to 0.523 and reduces boundary error from 0.202 m to 0.146 m compared with DiffCAD. These results indicate that the proposed framework improves navigation-oriented occupancy and obstacle-boundary recovery under CAD-library-based and segmentation-dependent single-view scene completion settings. Full article

Wet cleaning robots have seen a boost in popularity in recent years, with notable impact on their technical features and portfolio of functionalities. To improve cleaning results as well as to create unique selling points, robot manufacturers introduce and expand on new wet cleaning concepts such as self-regenerating roller mops, close-to-wall operation and floor sterilisation. This paper takes a narrative approach to provide an overview of the development of wet cleaning robots for household usage in the span of the last four years (2022–2025). During this period, significant advancements have been made to increase the wet cleaning potential in household robots, both wet & dry cleaning units and dedicated wet cleaning models. The review focuses on developments that directly enhance wet cleaning performance (e.g., mop kinematics, regeneration and hygiene functions) and deliberately excludes advances that are not specific to wet cleaning (e.g., battery chemistry or generic navigation). As part of the review process, the findings are checked against the current landscape of technical standardisation. Thus, the paper identifies normative gaps which have opened due to the absence of international technical standards for wet cleaning robots. It advises on filling these gaps by establishing and updating testing guidelines to address new developments. Full article

Although the traditional Gmapping algorithm incorporates optimized proposal distribution and resampling strategies within the RBPF-SLAM framework, it remains susceptible to particle degradation during intensive particle iterations. This degradation compromises map integrity and localization accuracy. To address this limitation, this study proposes an enhanced Gmapping system integrated with an improved particle swarm optimization (PSO) algorithm. The proposed PSO incorporates an adaptive inertia weight and a Gaussian distribution model to guide swarm dynamics, thereby effectively accelerating convergence. Furthermore, during the resampling phase, the system adopts an SDPR strategy to reduce computational complexity, shorten runtime, and alleviate particle degradation. The improved PSO algorithm was first validated through MATLAB R2022b simulations, and the integrated system was subsequently implemented and tested on a ROS-based Unmanned ground vehicle (UGV) platform within the Gazebo simulation environment (Gazebo Garden). From the results, compared with classical Gmapping using 50 particles, the proposed method using 50 particles reduces the ATE RMSE from 0.154 m to 0.104 m, corresponding to a 32.5% reduction. The RPE translation RMSE decreases by 31.0%, and the map-scale MAE decreases by 44.6%. The average time per frame is also slightly lower than Gmapping-50 because SDPR reduces the frequency and cost of full resampling. Experimental results demonstrate that the proposed system yields significant improvements in both accuracy and robustness for localization and environmental mapping. Full article

An emotional neural network-based global predefined-time sliding mode control (ENN-GPTSMC) method is proposed for an uncertain hybrid mechanism. To estimate and compensate for the lumped uncertainty including discontinuous friction, an emotional neural network is developed. Simultaneously, a predefined-time terminal sliding mode control (PTTSMC) uses the estimation value. The adjustable predefined-time performance parameters are then incorporated into the PTTSMC law to extend its attractiveness for the system states to the global domain, thereby solving the limitation of the existing PTTSMC that can only locally achieve the predefined-time convergence of the system states during the reaching phase. The fast convergence of system states is subsequently achieved by embedding an integer-power linear term and its derivative into the sliding manifold and PTTSMC law, respectively. Based on these, an ENN-GPTSMC algorithm is designed. Furthermore, the saturation function of a dynamic boundary layer with an adjustable thickness is designed to avoid the singularity of ENN-GPTSMC, thereby achieving no-singularity fast global predefined-time convergence of the system. Theoretical analysis shows the Lyapunov stability of the system. Finally, simulation and prototype experiments are used to verify the effectiveness of the proposed method. Full article

This work presents a literature review on interoperability in industrial robotics. The analysis of 45 selected studies reveals that existing approaches remain fragmented across communication, control abstraction, and semantic integration layers. The review synthesizes key developments in programming paradigms, communication technologies, and interoperability solutions in heterogeneous industrial environments. Based on the identified gaps, a conceptual interoperability framework, referred to as the Universal Robot Protocol (URP), is derived to support unified integration across system layers. URP is not proposed as an implemented protocol, but as a research-driven conceptual direction intended to integrate existing technologies within a coherent interoperability architecture. This contribution aims to support future research and the industrial adoption of interoperable robotic systems in Industry 4.0 and Industry 5.0 environments. Full article

Cooperative localization enables teams of robots to maintain better positioning in GNSS-denied environments by sharing state estimates and inter-robot range measurements to reduce the rate of proprioceptive odometry drift. In scenarios without fixed navigation beacons or pre-surveyed reference nodes, each robot functions as both a positioning client and a mobile ranging peer. A critical vulnerability in this architecture is silent fault propagation. A robot with a degraded localization solution may broadcast an incorrect, often overconfident position estimate, corrupting its peers’ localization. Classical Global Navigation Satellite System (GNSS) Receiver Autonomous Integrity Monitoring (RAIM) methods are ineffective in this context because meter-scale inter-robot separations introduce strong geometric nonlinearity and unstable Geometric Dilution of Precision (GDOP), resulting in scattered subset solutions rather than the coherent, biased clusters that RAIM is designed to detect. This paper addresses this vulnerability by proposing a two-stage distributed Fault Detection and Identification (FDI) architecture for peer-to-peer ranging-based cooperative localization. The first stage applies a global chi-square test on Weighted Least-Squares trilateration residuals to detect the presence of a fault. The second stage identifies the faulty robot by computing Leave-One-Out and Leave-Two-Out subset solutions, which are then partitioned using a clustering algorithm. The cluster that exempts measurements from the faulty robot is identified using either a maximum-cardinality or a minimum-variance criterion. A decentralized voting protocol that requires at least two independent corroborations is then employed for network-wide fault declaration. Monte Carlo simulations show that the clustering-based identification method outperforms classical residual-based methods across multiple fault types, with results reported for the planar (2D) case. No single clustering configuration dominates in terms of identification performance across all tested fault conditions, as performance varies with the fault profile. The proposed architecture operates fully in a distributed manner, requiring only the exchange of position estimates, covariances, and binary votes. Full article

In the anti-sway control of variable-length double-pendulum gantry cranes, traditional sliding mode control relies on high switching gains, which can cause chattering. Additionally, the introduction of neural networks presents challenges in tuning high-dimensional parameters. To address these issues, this study proposes an adaptive hierarchical sliding mode control strategy based on an RBF neural network and particle swarm optimization. First, a low-energy-dissipation dynamic model is established without the small-angle assumption. Second, a composite hierarchical sliding surface is designed to achieve multi-objective decoupling, and an RBF neural network is utilized to approximate the system’s unknown dynamics online, thereby reducing switching gains and suppressing chattering. The asymptotic stability of the closed-loop system is proven based on Lyapunov theory. Finally, a particle swarm optimization algorithm is introduced to achieve automated, high-precision matching of high-dimensional controller parameters. Simulation results indicate that the control method designed in this paper can achieve automatic matching of high-dimensional parameters, effectively resolving the chattering issue in sliding mode control. Furthermore, under wide-range parameter perturbations and external multi-source disturbances, the controller exhibits strong robustness and demonstrates excellent positioning and anti-chattering performance. Full article

Indoor service robots often face challenges in target localization and robotic grasping under cluttered backgrounds, partial occlusion, and viewpoint variations. To address these issues, this study proposes a vision-guided robotic grasping framework based on an improved feature matching algorithm termed Mahalanobis-accelerated Scale-Invariant Feature Transform Plus (MSIFT+). The proposed method integrates Mahalanobis distance metric reconstruction with a dynamic Best-Bin-First (BBF) search strategy to improve matching robustness and computational efficiency. A multi-scenario indoor dataset was constructed to evaluate the proposed method under rotational variation, weak-texture, and partial occlusion conditions. The results demonstrate that the MSIFT+ algorithm significantly outperforms other methods in cross-scenario consistency and adaptability to weakly textured targets. Furthermore, a binocular vision-guided robotic grasping system was developed and validated through practical robotic experiments. Experimental results confirm that the MSIFT+ algorithm enhances detection performance for small and clustered targets in complex environments. The proposed framework provides an effective and reliable solution for robotic object localization and grasping in complex indoor environments. Full article

Autonomous mobile robots (AMRs) are increasingly used in warehouse intralogistics to improve material flow, flexibility, productivity, and operational continuity. However, selecting an appropriate AMR is no longer limited to mechanical performance or acquisition cost, since modern warehouse robots operate as networked cyber-physical systems that must interact with enterprise software, fleet management platforms, communication infrastructures, and cybersecurity mechanisms. This study proposes an integrated Pythagorean fuzzy multi-criteria decision-making (MCDM) framework for evaluating AMR alternatives in warehouse operations by jointly considering economic, technical, physical, software-related, integration-oriented, and security-related criteria. Expert judgments obtained from eight specialists, including four academics and four private-sector professionals, were modeled using Pythagorean fuzzy numbers to capture uncertainty and hesitation in linguistic assessments. The Pythagorean Fuzzy Indifference Threshold-Based Attribute Ratio Analysis (PF-ITARA) method was employed to determine criterion weights based on threshold-sensitive discrimination among alternatives, while Pythagorean Fuzzy VIšekriterijumsko KOmpromisno Rangiranje (PF-VIKOR) was used to rank four AMR alternatives according to a compromise solution logic. The results show that investment cost, maneuverability, total cost of ownership, integration and validation requirements, and ease of programming and commissioning are the most influential criteria. Cybersecurity-related criteria, particularly data confidentiality, system integrity, monitoring and incident response readiness, authentication control, and role-based access control, also received notable importance levels. Among the evaluated alternatives, MiR250 achieved the best overall performance and emerged as the most suitable compromise solution, followed by OMRON LD-250, HIKROBOT Forklift AGV, and KUKA KMP 600-S diffDrive. The proposed framework provides a transparent and practically applicable decision-support tool for AMR procurement by integrating operational performance, digital interoperability, and cybersecurity readiness into a unified evaluation structure. Full article

UAV path planning in complex three-dimensional obstacle environments requires a balance between search efficiency and flight feasibility. However, existing RRT*-based methods often fail to satisfy this requirement, as their random sampling lacks directional guidance and makes limited use of environmental information. To this end, this paper proposes an environment-aware cooperative bidirectional RRT* algorithm (EAC-Bi-RRT*). In the sampling stage, the sampling probability of each direction is adaptively adjusted according to the obstacle distribution across 26 directional sectors and the relative goal orientation, so that the search receives stronger directional guidance. During bidirectional expansion, the two trees are assigned leader and follower roles according to the local expandability on the start and goal sides, and their cooperative search is combined with an environment-adaptive step size and a climbing-angle constraint to balance search efficiency and flight reachability. When an expanding node approaches an obstacle, a repulsive-only local directional correction suppresses oscillation, and the initial path is then smoothed by a curvature-constrained B-spline to form a continuous flight trajectory. Across all test scenarios, EAC-Bi-RRT* achieves a 100% planning success rate. Compared with the baseline algorithms, it reduces planning time by approximately 54–90% and path length by approximately 5–18% while maintaining low average turning angles, which demonstrates competitive overall performance. Full article

In complex obstacle environments, the RRT* algorithm, an asymptotically optimal variant of the Rapidly exploring Random Tree (RRT), and its related variants often suffer from slow generation of the initial feasible solution, unstable sampling efficiency, and high computational costs associated with nearest-neighbor search and collision checking. To address these issues, this paper proposes a coordinated lightweight improved RRT* algorithm. First, a bidirectional growth mechanism combined with goal-biased sampling is introduced to enhance search directionality and improve the efficiency of initial feasible path generation. After an initial path is obtained, informed elliptical sampling is adopted, and the sampling weights are adaptively allocated among the elliptical region, the global space, and goal-biased sampling, thereby balancing local convergence and global exploration. Furthermore, a spatial-hash structure with a dynamic neighborhood radius is employed to accelerate nearest-neighbor search, while lazy collision checking and a two-stage collision-detection mechanism are incorporated into parent selection to reduce redundant expansions and unnecessary exact collision checks. Simulation results in mixed-type and single-type obstacle environments show that the proposed algorithm improves planning efficiency while maintaining competitive path quality. These results demonstrate that the proposed method has good engineering applicability for global path planning in complex environments. Full article